Overview

Dataset statistics

Number of variables29
Number of observations1581
Missing cells1611
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory358.3 KiB
Average record size in memory232.1 B

Variable types

Categorical13
Numeric16

Warnings

UBPP Laborat has constant value "0" Constant
Nama Pelatihan has a high cardinality: 1370 distinct values High cardinality
Location has a high cardinality: 102 distinct values High cardinality
Tgl Mulai has a high cardinality: 271 distinct values High cardinality
Tgl Selesai has a high cardinality: 310 distinct values High cardinality
JML Peserta is highly correlated with JML Confirmed and 1 other fieldsHigh correlation
JML Confirmed is highly correlated with JML Peserta and 1 other fieldsHigh correlation
JML Peserta Hadir is highly correlated with JML Peserta and 1 other fieldsHigh correlation
UBPP Akom is highly correlated with UBPP KafetariaHigh correlation
UBPP Sarana is highly correlated with UBPP Penyelenggaraan and 4 other fieldsHigh correlation
UBPP Penyelenggaraan is highly correlated with UBPP Sarana and 4 other fieldsHigh correlation
UBPP Kafetaria is highly correlated with UBPP AkomHigh correlation
UBPP Materi is highly correlated with UBPP Sarana and 4 other fieldsHigh correlation
Customer Satisfaction is highly correlated with UBPP Sarana and 4 other fieldsHigh correlation
First Response & Average Handling is highly correlated with UBPP Sarana and 4 other fieldsHigh correlation
Customer Effort Score is highly correlated with UBPP Sarana and 4 other fieldsHigh correlation
Provider is highly correlated with Provider Category and 1 other fieldsHigh correlation
Provider Category is highly correlated with Provider and 1 other fieldsHigh correlation
UBPP Laborat is highly correlated with Provider and 7 other fieldsHigh correlation
Category is highly correlated with UBPP LaboratHigh correlation
Lokasi Pelatihan is highly correlated with UBPP LaboratHigh correlation
Tipe is highly correlated with UBPP LaboratHigh correlation
Academy Event is highly correlated with UBPP LaboratHigh correlation
Event Type is highly correlated with UBPP LaboratHigh correlation
Status is highly correlated with UBPP LaboratHigh correlation
Tipe has 686 (43.4%) missing values Missing
Lokasi Pelatihan has 134 (8.5%) missing values Missing
Provider has 101 (6.4%) missing values Missing
Academy Event has 122 (7.7%) missing values Missing
Location has 67 (4.2%) missing values Missing
Provider Category has 342 (21.6%) missing values Missing
Event Type has 55 (3.5%) missing values Missing
Status has 62 (3.9%) missing values Missing
UBPP Inst has 42 (2.7%) missing values Missing
Nama Pelatihan is uniformly distributed Uniform
Objid Pelatihan has unique values Unique
JML Peserta has 278 (17.6%) zeros Zeros
JML Confirmed has 613 (38.8%) zeros Zeros
JML Peserta Hadir has 520 (32.9%) zeros Zeros
JML UBPP Inst has 748 (47.3%) zeros Zeros
JML UBPP Delivery has 645 (40.8%) zeros Zeros
UBPP Inst has 761 (48.1%) zeros Zeros
UBPP Akom has 1030 (65.1%) zeros Zeros
UBPP Sarana has 646 (40.9%) zeros Zeros
UBPP Penyelenggaraan has 645 (40.8%) zeros Zeros
UBPP Kafetaria has 1030 (65.1%) zeros Zeros
UBPP Materi has 646 (40.9%) zeros Zeros
Net Promotor Score has 693 (43.8%) zeros Zeros
Customer Satisfaction has 646 (40.9%) zeros Zeros
First Response & Average Handling has 646 (40.9%) zeros Zeros
Customer Effort Score has 646 (40.9%) zeros Zeros

Reproduction

Analysis started2021-02-03 06:00:38.150633
Analysis finished2021-02-03 06:01:59.662990
Duration1 minute and 21.51 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Nama Pelatihan
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1370
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
Assessment JT Dir BUMN
 
10
Machine Learning with Python
 
10
Assesment JT BP 1
 
8
Marketing 4.0
 
7
Wholesale International Business Advisor
 
6
Other values (1365)
1540 

Length

Max length95
Median length29
Mean length28.7368754
Min length3

Characters and Unicode

Total characters45433
Distinct characters77
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1248 ?
Unique (%)78.9%

Sample

1st rowSocial Media Ads & Analytic
2nd rowCISM
3rd rowProgrammer Development Course Amoeba
4th rowBusiness Analytics Amoeba
5th rowOnline UX Design Course Amoeba
ValueCountFrequency (%)
Assessment JT Dir BUMN10
 
0.6%
Machine Learning with Python10
 
0.6%
Assesment JT BP 18
 
0.5%
Marketing 4.07
 
0.4%
Wholesale International Business Advisor6
 
0.4%
TRShoot GGN HSI:ONT,OLT,Metro,BRAS,PE SP5
 
0.3%
Assesment JT BP 25
 
0.3%
FINON5
 
0.3%
Bangmat GPMP5
 
0.3%
Junior executive infrastructure program4
 
0.3%
Other values (1360)1516
95.9%
2021-02-03T13:02:00.404614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
200
 
2.8%
batch186
 
2.6%
bangmat115
 
1.6%
for114
 
1.6%
299
 
1.4%
am86
 
1.2%
176
 
1.1%
202066
 
0.9%
management65
 
0.9%
business65
 
0.9%
Other values (1508)6019
84.9%

Most occurring characters

ValueCountFrequency (%)
5532
 
12.2%
a3293
 
7.2%
e3291
 
7.2%
n2776
 
6.1%
i2691
 
5.9%
t2256
 
5.0%
r1988
 
4.4%
o1932
 
4.3%
s1668
 
3.7%
l1111
 
2.4%
Other values (67)18895
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28574
62.9%
Uppercase Letter9406
 
20.7%
Space Separator5532
 
12.2%
Decimal Number1187
 
2.6%
Other Punctuation376
 
0.8%
Dash Punctuation173
 
0.4%
Open Punctuation93
 
0.2%
Close Punctuation89
 
0.2%
Control1
 
< 0.1%
Modifier Symbol1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
P930
 
9.9%
A872
 
9.3%
C763
 
8.1%
S743
 
7.9%
M742
 
7.9%
B711
 
7.6%
T703
 
7.5%
I513
 
5.5%
D437
 
4.6%
O386
 
4.1%
Other values (16)2606
27.7%
ValueCountFrequency (%)
a3293
11.5%
e3291
11.5%
n2776
9.7%
i2691
9.4%
t2256
 
7.9%
r1988
 
7.0%
o1932
 
6.8%
s1668
 
5.8%
l1111
 
3.9%
c1108
 
3.9%
Other values (16)6460
22.6%
ValueCountFrequency (%)
2339
28.6%
0210
17.7%
1182
15.3%
3118
 
9.9%
499
 
8.3%
593
 
7.8%
665
 
5.5%
748
 
4.0%
919
 
1.6%
814
 
1.2%
ValueCountFrequency (%)
&169
44.9%
:58
 
15.4%
#53
 
14.1%
,45
 
12.0%
.31
 
8.2%
/16
 
4.3%
'3
 
0.8%
"1
 
0.3%
ValueCountFrequency (%)
5532
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
(93
100.0%
ValueCountFrequency (%)
-173
100.0%
ValueCountFrequency (%)
)89
100.0%
ValueCountFrequency (%)
`1
100.0%
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin37980
83.6%
Common7453
 
16.4%

Most frequent character per script

ValueCountFrequency (%)
a3293
 
8.7%
e3291
 
8.7%
n2776
 
7.3%
i2691
 
7.1%
t2256
 
5.9%
r1988
 
5.2%
o1932
 
5.1%
s1668
 
4.4%
l1111
 
2.9%
c1108
 
2.9%
Other values (42)15866
41.8%
ValueCountFrequency (%)
5532
74.2%
2339
 
4.5%
0210
 
2.8%
1182
 
2.4%
-173
 
2.3%
&169
 
2.3%
3118
 
1.6%
499
 
1.3%
(93
 
1.2%
593
 
1.2%
Other values (15)445
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII45433
100.0%

Most frequent character per block

ValueCountFrequency (%)
5532
 
12.2%
a3293
 
7.2%
e3291
 
7.2%
n2776
 
6.1%
i2691
 
5.9%
t2256
 
5.0%
r1988
 
4.4%
o1932
 
4.3%
s1668
 
3.7%
l1111
 
2.4%
Other values (67)18895
41.6%

Objid Pelatihan
Real number (ℝ≥0)

UNIQUE

Distinct1581
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82796908.33
Minimum80110780
Maximum90004562
Zeros0
Zeros (%)0.0%
Memory size12.5 KiB
2021-02-03T13:02:00.656048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum80110780
5-th percentile80111833
Q180112665
median80113458
Q390003748
95-th percentile90004398
Maximum90004562
Range9893782
Interquartile range (IQR)9891083

Descriptive statistics

Standard deviation4399535.407
Coefficient of variation (CV)0.05313646965
Kurtosis-0.9414716628
Mean82796908.33
Median Absolute Deviation (MAD)864
Skewness1.02942742
Sum1.309019121 × 1011
Variance1.93559118 × 1013
MonotocityNot monotonic
2021-02-03T13:02:00.902879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801132931
 
0.1%
801130171
 
0.1%
801130311
 
0.1%
801130331
 
0.1%
801130371
 
0.1%
801130391
 
0.1%
900045181
 
0.1%
900044821
 
0.1%
801130451
 
0.1%
801130491
 
0.1%
Other values (1571)1571
99.4%
ValueCountFrequency (%)
801107801
0.1%
801112091
0.1%
801112141
0.1%
801112161
0.1%
801112171
0.1%
ValueCountFrequency (%)
900045621
0.1%
900045581
0.1%
900045561
0.1%
900045541
0.1%
900045521
0.1%

Category
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
LAT
1152 
NON LAT
429 

Length

Max length7
Median length3
Mean length4.085388994
Min length3

Characters and Unicode

Total characters6459
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLAT
2nd rowLAT
3rd rowLAT
4th rowLAT
5th rowLAT
ValueCountFrequency (%)
LAT1152
72.9%
NON LAT429
 
27.1%
2021-02-03T13:02:01.326525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-03T13:02:01.454063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
lat1581
78.7%
non429
 
21.3%

Most occurring characters

ValueCountFrequency (%)
L1581
24.5%
A1581
24.5%
T1581
24.5%
N858
13.3%
O429
 
6.6%
429
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6030
93.4%
Space Separator429
 
6.6%

Most frequent character per category

ValueCountFrequency (%)
L1581
26.2%
A1581
26.2%
T1581
26.2%
N858
14.2%
O429
 
7.1%
ValueCountFrequency (%)
429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6030
93.4%
Common429
 
6.6%

Most frequent character per script

ValueCountFrequency (%)
L1581
26.2%
A1581
26.2%
T1581
26.2%
N858
14.2%
O429
 
7.1%
ValueCountFrequency (%)
429
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6459
100.0%

Most frequent character per block

ValueCountFrequency (%)
L1581
24.5%
A1581
24.5%
T1581
24.5%
N858
13.3%
O429
 
6.6%
429
 
6.6%

Tipe
Categorical

HIGH CORRELATION
MISSING

Distinct21
Distinct (%)2.3%
Missing686
Missing (%)43.4%
Memory size12.5 KiB
Pengembangan Materi
299 
Test Online
148 
Seminar
131 
Project
118 
Virtual Classroom
66 
Other values (16)
133 

Length

Max length29
Median length11
Mean length13.67932961
Min length3

Characters and Unicode

Total characters12243
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowVirtual Classroom
2nd rowE-learning
3rd rowVideo Learning
4th rowE-learning
5th rowVirtual Classroom
ValueCountFrequency (%)
Pengembangan Materi299
18.9%
Test Online148
 
9.4%
Seminar131
 
8.3%
Project118
 
7.5%
Virtual Classroom66
 
4.2%
E-learning24
 
1.5%
Sharing Session18
 
1.1%
Sharing Session / Bedah Buku17
 
1.1%
Online Forum14
 
0.9%
Mentoring10
 
0.6%
Other values (11)50
 
3.2%
(Missing)686
43.4%
2021-02-03T13:02:01.801870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pengembangan299
19.1%
materi299
19.1%
online162
10.3%
test148
9.4%
seminar131
8.4%
project118
 
7.5%
virtual66
 
4.2%
classroom66
 
4.2%
session35
 
2.2%
sharing35
 
2.2%
Other values (27)208
13.3%

Most occurring characters

ValueCountFrequency (%)
e1610
13.2%
n1579
12.9%
a1295
10.6%
i841
 
6.9%
r793
 
6.5%
g703
 
5.7%
672
 
5.5%
t668
 
5.5%
m531
 
4.3%
P425
 
3.5%
Other values (30)3126
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9932
81.1%
Uppercase Letter1570
 
12.8%
Space Separator672
 
5.5%
Dash Punctuation30
 
0.2%
Other Punctuation23
 
0.2%
Open Punctuation8
 
0.1%
Close Punctuation8
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e1610
16.2%
n1579
15.9%
a1295
13.0%
i841
8.5%
r793
8.0%
g703
7.1%
t668
6.7%
m531
 
5.3%
s398
 
4.0%
o364
 
3.7%
Other values (10)1150
11.6%
ValueCountFrequency (%)
P425
27.1%
M322
20.5%
S202
12.9%
O169
 
10.8%
T164
 
10.4%
C82
 
5.2%
V74
 
4.7%
B44
 
2.8%
E30
 
1.9%
L18
 
1.1%
Other values (5)40
 
2.5%
ValueCountFrequency (%)
672
100.0%
ValueCountFrequency (%)
-30
100.0%
ValueCountFrequency (%)
/23
100.0%
ValueCountFrequency (%)
(8
100.0%
ValueCountFrequency (%)
)8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11502
93.9%
Common741
 
6.1%

Most frequent character per script

ValueCountFrequency (%)
e1610
14.0%
n1579
13.7%
a1295
11.3%
i841
 
7.3%
r793
 
6.9%
g703
 
6.1%
t668
 
5.8%
m531
 
4.6%
P425
 
3.7%
s398
 
3.5%
Other values (25)2659
23.1%
ValueCountFrequency (%)
672
90.7%
-30
 
4.0%
/23
 
3.1%
(8
 
1.1%
)8
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12243
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1610
13.2%
n1579
12.9%
a1295
10.6%
i841
 
6.9%
r793
 
6.5%
g703
 
5.7%
672
 
5.5%
t668
 
5.5%
m531
 
4.3%
P425
 
3.5%
Other values (30)3126
25.5%

Lokasi Pelatihan
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)1.2%
Missing134
Missing (%)8.5%
Memory size12.5 KiB
Bandung
930 
Jakarta
231 
Makassar
125 
Surabaya
 
46
Semarang
 
42
Other values (13)
 
73

Length

Max length16
Median length7
Mean length7.121630961
Min length4

Characters and Unicode

Total characters10305
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st rowBandung
2nd rowBandung
3rd rowBandung
4th rowBandung
5th rowBandung
ValueCountFrequency (%)
Bandung930
58.8%
Jakarta231
 
14.6%
Makassar125
 
7.9%
Surabaya46
 
2.9%
Semarang42
 
2.7%
Medan42
 
2.7%
BANDUNG12
 
0.8%
Yogyakarta4
 
0.3%
Jakarta Selatan3
 
0.2%
Balikpapan2
 
0.1%
Other values (8)10
 
0.6%
(Missing)134
 
8.5%
2021-02-03T13:02:02.227141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bandung942
64.9%
jakarta236
 
16.3%
makassar125
 
8.6%
surabaya46
 
3.2%
medan42
 
2.9%
semarang42
 
2.9%
yogyakarta4
 
0.3%
selatan3
 
0.2%
balikpapan2
 
0.1%
bogor2
 
0.1%
Other values (7)7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a2308
22.4%
n1954
19.0%
g981
9.5%
u979
9.5%
d973
9.4%
B948
9.2%
r455
 
4.4%
k366
 
3.6%
s250
 
2.4%
t243
 
2.4%
Other values (22)848
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8766
85.1%
Uppercase Letter1535
 
14.9%
Space Separator4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2308
26.3%
n1954
22.3%
g981
11.2%
u979
11.2%
d973
11.1%
r455
 
5.2%
k366
 
4.2%
s250
 
2.9%
t243
 
2.8%
e90
 
1.0%
Other values (7)167
 
1.9%
ValueCountFrequency (%)
B948
61.8%
J236
 
15.4%
M169
 
11.0%
S91
 
5.9%
N24
 
1.6%
A18
 
1.2%
D12
 
0.8%
U12
 
0.8%
G12
 
0.8%
Y4
 
0.3%
Other values (4)9
 
0.6%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10301
> 99.9%
Common4
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
a2308
22.4%
n1954
19.0%
g981
9.5%
u979
9.5%
d973
9.4%
B948
9.2%
r455
 
4.4%
k366
 
3.6%
s250
 
2.4%
t243
 
2.4%
Other values (21)844
 
8.2%
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10305
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2308
22.4%
n1954
19.0%
g981
9.5%
u979
9.5%
d973
9.4%
B948
9.2%
r455
 
4.4%
k366
 
3.6%
s250
 
2.4%
t243
 
2.4%
Other values (22)848
 
8.2%

Provider
Categorical

HIGH CORRELATION
MISSING

Distinct17
Distinct (%)1.1%
Missing101
Missing (%)6.4%
Memory size12.5 KiB
Learning Area 3 / LO
388 
Learning Area 3 / LO Bandung
311 
Learning Area 7 Makassar
222 
Learning Area 2 Jakarta
212 
Learning Area 3 Bandung
88 
Other values (12)
259 

Length

Max length28
Median length23
Mean length23.17905405
Min length9

Characters and Unicode

Total characters34305
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowLearning Area 2 Jakarta
2nd rowLearning Area 3 / LO
3rd rowLearning Area 7 Makassar
4th rowLearning Area 7 Makassar
5th rowLearning Area 7 Makassar
ValueCountFrequency (%)
Learning Area 3 / LO388
24.5%
Learning Area 3 / LO Bandung311
19.7%
Learning Area 7 Makassar222
14.0%
Learning Area 2 Jakarta212
13.4%
Learning Area 3 Bandung88
 
5.6%
Learning Area 5 Surabaya82
 
5.2%
Learning Area 4 Semarang65
 
4.1%
Learning Area 1 Medan62
 
3.9%
Dalam Negeri27
 
1.7%
Learning Operation Bandung6
 
0.4%
Other values (7)17
 
1.1%
(Missing)101
 
6.4%
2021-02-03T13:02:02.659795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
learning1448
21.1%
area1442
21.0%
3789
11.5%
699
10.2%
lo699
10.2%
bandung405
 
5.9%
7225
 
3.3%
makassar222
 
3.2%
2213
 
3.1%
jakarta212
 
3.1%
Other values (17)501
 
7.3%

Most occurring characters

ValueCountFrequency (%)
5375
15.7%
a5110
14.9%
n3847
11.2%
r3515
10.2%
e3083
9.0%
L2147
 
6.3%
g1945
 
5.7%
i1488
 
4.3%
A1444
 
4.2%
3789
 
2.3%
Other values (33)5562
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21373
62.3%
Uppercase Letter5413
 
15.8%
Space Separator5375
 
15.7%
Decimal Number1445
 
4.2%
Other Punctuation699
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
a5110
23.9%
n3847
18.0%
r3515
16.4%
e3083
14.4%
g1945
 
9.1%
i1488
 
7.0%
u488
 
2.3%
d470
 
2.2%
s444
 
2.1%
k438
 
2.0%
Other values (10)545
 
2.5%
ValueCountFrequency (%)
L2147
39.7%
A1444
26.7%
O705
 
13.0%
B409
 
7.6%
M285
 
5.3%
J212
 
3.9%
S147
 
2.7%
D27
 
0.5%
N27
 
0.5%
P3
 
0.1%
Other values (4)7
 
0.1%
ValueCountFrequency (%)
3789
54.6%
7225
 
15.6%
2213
 
14.7%
582
 
5.7%
167
 
4.6%
465
 
4.5%
64
 
0.3%
ValueCountFrequency (%)
5375
100.0%
ValueCountFrequency (%)
/699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin26786
78.1%
Common7519
 
21.9%

Most frequent character per script

ValueCountFrequency (%)
a5110
19.1%
n3847
14.4%
r3515
13.1%
e3083
11.5%
L2147
8.0%
g1945
 
7.3%
i1488
 
5.6%
A1444
 
5.4%
O705
 
2.6%
u488
 
1.8%
Other values (24)3014
11.3%
ValueCountFrequency (%)
5375
71.5%
3789
 
10.5%
/699
 
9.3%
7225
 
3.0%
2213
 
2.8%
582
 
1.1%
167
 
0.9%
465
 
0.9%
64
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII34305
100.0%

Most frequent character per block

ValueCountFrequency (%)
5375
15.7%
a5110
14.9%
n3847
11.2%
r3515
10.2%
e3083
9.0%
L2147
 
6.3%
g1945
 
5.7%
i1488
 
4.3%
A1444
 
4.2%
3789
 
2.3%
Other values (33)5562
16.2%

Academy Event
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)0.8%
Missing122
Missing (%)7.7%
Memory size12.5 KiB
DSP
323 
BUSINNESS ENABLER
301 
CONSUMER
274 
NITS
173 
ENTERPRISE
156 
Other values (6)
232 

Length

Max length18
Median length8
Mean length9.549006169
Min length3

Characters and Unicode

Total characters13932
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowCONSUMER
2nd rowNITS
3rd rowDSP
4th rowDSP
5th rowDSP
ValueCountFrequency (%)
DSP323
20.4%
BUSINNESS ENABLER301
19.0%
CONSUMER274
17.3%
NITS173
10.9%
ENTERPRISE156
9.9%
WS & INTERNATIONAL143
9.0%
LEADERSHIP75
 
4.7%
KMCS7
 
0.4%
GOFM4
 
0.3%
PNC,LO, dan GS2
 
0.1%
(Missing)122
 
7.7%
2021-02-03T13:02:03.127263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dsp323
15.8%
businness301
14.7%
enabler301
14.7%
consumer274
13.4%
nits173
8.4%
enterprise156
7.6%
international143
7.0%
ws143
7.0%
143
7.0%
leadership75
 
3.7%
Other values (6)18
 
0.9%

Most occurring characters

ValueCountFrequency (%)
S2056
14.8%
E1939
13.9%
N1937
13.9%
R1105
 
7.9%
I992
 
7.1%
A662
 
4.8%
T615
 
4.4%
B603
 
4.3%
591
 
4.2%
U575
 
4.1%
Other values (16)2857
20.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13188
94.7%
Space Separator591
 
4.2%
Other Punctuation147
 
1.1%
Lowercase Letter6
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
S2056
15.6%
E1939
14.7%
N1937
14.7%
R1105
8.4%
I992
7.5%
A662
 
5.0%
T615
 
4.7%
B603
 
4.6%
U575
 
4.4%
P556
 
4.2%
Other values (10)2148
16.3%
ValueCountFrequency (%)
d2
33.3%
a2
33.3%
n2
33.3%
ValueCountFrequency (%)
&143
97.3%
,4
 
2.7%
ValueCountFrequency (%)
591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13194
94.7%
Common738
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
S2056
15.6%
E1939
14.7%
N1937
14.7%
R1105
8.4%
I992
7.5%
A662
 
5.0%
T615
 
4.7%
B603
 
4.6%
U575
 
4.4%
P556
 
4.2%
Other values (13)2154
16.3%
ValueCountFrequency (%)
591
80.1%
&143
 
19.4%
,4
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII13932
100.0%

Most frequent character per block

ValueCountFrequency (%)
S2056
14.8%
E1939
13.9%
N1937
13.9%
R1105
 
7.9%
I992
 
7.1%
A662
 
4.8%
T615
 
4.4%
B603
 
4.3%
591
 
4.2%
U575
 
4.1%
Other values (16)2857
20.5%

Location
Categorical

HIGH CARDINALITY
MISSING

Distinct102
Distinct (%)6.7%
Missing67
Missing (%)4.2%
Memory size12.5 KiB
Online (Lokasi Masing-Masing)
1044 
Learning Area 3 Bandung
 
58
LA 3
 
52
LA 2
 
49
Learning Area 2 Jakarta
 
35
Other values (97)
276 

Length

Max length45
Median length29
Mean length25.45970938
Min length3

Characters and Unicode

Total characters38546
Distinct characters63
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)3.5%

Sample

1st rowOnline (Lokasi Masing-Masing)
2nd rowOnline (Lokasi Masing-Masing)
3rd rowOnline (Lokasi Masing-Masing)
4th rowOnline (Lokasi Masing-Masing)
5th rowOnline (Lokasi Masing-Masing)
ValueCountFrequency (%)
Online (Lokasi Masing-Masing)1044
66.0%
Learning Area 3 Bandung58
 
3.7%
LA 352
 
3.3%
LA 249
 
3.1%
Learning Area 2 Jakarta35
 
2.2%
LA-323
 
1.5%
TELKOM HRAS BANDUNG17
 
1.1%
Telkom Corporate University16
 
1.0%
Learning Area 4 Semarang15
 
0.9%
TLT (Telkom Landmark Tower)13
 
0.8%
Other values (92)192
 
12.1%
(Missing)67
 
4.2%
2021-02-03T13:02:03.699917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
online1046
22.8%
lokasi1044
22.8%
masing-masing1044
22.8%
area132
 
2.9%
learning131
 
2.9%
3110
 
2.4%
la105
 
2.3%
bandung96
 
2.1%
287
 
1.9%
jakarta86
 
1.9%
Other values (164)704
15.4%

Most occurring characters

ValueCountFrequency (%)
n4799
12.5%
i4476
11.6%
a4137
10.7%
s3246
 
8.4%
3078
 
8.0%
g2382
 
6.2%
M2143
 
5.6%
e1658
 
4.3%
L1370
 
3.6%
o1264
 
3.3%
Other values (53)9993
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter26072
67.6%
Uppercase Letter5911
 
15.3%
Space Separator3078
 
8.0%
Dash Punctuation1073
 
2.8%
Open Punctuation1067
 
2.8%
Close Punctuation1062
 
2.8%
Decimal Number269
 
0.7%
Other Punctuation14
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
M2143
36.3%
L1370
23.2%
O1070
18.1%
A357
 
6.0%
T134
 
2.3%
B122
 
2.1%
J92
 
1.6%
S87
 
1.5%
C65
 
1.1%
H64
 
1.1%
Other values (14)407
 
6.9%
ValueCountFrequency (%)
n4799
18.4%
i4476
17.2%
a4137
15.9%
s3246
12.5%
g2382
9.1%
e1658
 
6.4%
o1264
 
4.8%
k1228
 
4.7%
l1186
 
4.5%
r600
 
2.3%
Other values (14)1096
 
4.2%
ValueCountFrequency (%)
3133
49.4%
288
32.7%
418
 
6.7%
510
 
3.7%
77
 
2.6%
16
 
2.2%
65
 
1.9%
82
 
0.7%
ValueCountFrequency (%)
,10
71.4%
.2
 
14.3%
&2
 
14.3%
ValueCountFrequency (%)
3078
100.0%
ValueCountFrequency (%)
(1067
100.0%
ValueCountFrequency (%)
-1073
100.0%
ValueCountFrequency (%)
)1062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31983
83.0%
Common6563
 
17.0%

Most frequent character per script

ValueCountFrequency (%)
n4799
15.0%
i4476
14.0%
a4137
12.9%
s3246
10.1%
g2382
7.4%
M2143
6.7%
e1658
 
5.2%
L1370
 
4.3%
o1264
 
4.0%
k1228
 
3.8%
Other values (38)5280
16.5%
ValueCountFrequency (%)
3078
46.9%
-1073
 
16.3%
(1067
 
16.3%
)1062
 
16.2%
3133
 
2.0%
288
 
1.3%
418
 
0.3%
,10
 
0.2%
510
 
0.2%
77
 
0.1%
Other values (5)17
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII38546
100.0%

Most frequent character per block

ValueCountFrequency (%)
n4799
12.5%
i4476
11.6%
a4137
10.7%
s3246
 
8.4%
3078
 
8.0%
g2382
 
6.2%
M2143
 
5.6%
e1658
 
4.3%
L1370
 
3.6%
o1264
 
3.3%
Other values (53)9993
25.9%

Provider Category
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing342
Missing (%)21.6%
Memory size12.5 KiB
Internal
1083 
External
156 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters9912
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternal
2nd rowInternal
3rd rowInternal
4th rowInternal
5th rowInternal
ValueCountFrequency (%)
Internal1083
68.5%
External156
 
9.9%
(Missing)342
 
21.6%
2021-02-03T13:02:04.160660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-03T13:02:04.336424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
internal1083
87.4%
external156
 
12.6%

Most occurring characters

ValueCountFrequency (%)
n2322
23.4%
t1239
12.5%
e1239
12.5%
r1239
12.5%
a1239
12.5%
l1239
12.5%
I1083
10.9%
E156
 
1.6%
x156
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8673
87.5%
Uppercase Letter1239
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
n2322
26.8%
t1239
14.3%
e1239
14.3%
r1239
14.3%
a1239
14.3%
l1239
14.3%
x156
 
1.8%
ValueCountFrequency (%)
I1083
87.4%
E156
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin9912
100.0%

Most frequent character per script

ValueCountFrequency (%)
n2322
23.4%
t1239
12.5%
e1239
12.5%
r1239
12.5%
a1239
12.5%
l1239
12.5%
I1083
10.9%
E156
 
1.6%
x156
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9912
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2322
23.4%
t1239
12.5%
e1239
12.5%
r1239
12.5%
a1239
12.5%
l1239
12.5%
I1083
10.9%
E156
 
1.6%
x156
 
1.6%

Event Type
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing55
Missing (%)3.5%
Memory size12.5 KiB
Internal
1154 
Public
372 

Length

Max length8
Median length8
Mean length7.512450852
Min length6

Characters and Unicode

Total characters11464
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternal
2nd rowPublic
3rd rowInternal
4th rowInternal
5th rowInternal
ValueCountFrequency (%)
Internal1154
73.0%
Public372
 
23.5%
(Missing)55
 
3.5%
2021-02-03T13:02:04.909898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-03T13:02:05.144017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
internal1154
75.6%
public372
 
24.4%

Most occurring characters

ValueCountFrequency (%)
n2308
20.1%
l1526
13.3%
I1154
10.1%
t1154
10.1%
e1154
10.1%
r1154
10.1%
a1154
10.1%
P372
 
3.2%
u372
 
3.2%
b372
 
3.2%
Other values (2)744
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9938
86.7%
Uppercase Letter1526
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
n2308
23.2%
l1526
15.4%
t1154
11.6%
e1154
11.6%
r1154
11.6%
a1154
11.6%
u372
 
3.7%
b372
 
3.7%
i372
 
3.7%
c372
 
3.7%
ValueCountFrequency (%)
I1154
75.6%
P372
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin11464
100.0%

Most frequent character per script

ValueCountFrequency (%)
n2308
20.1%
l1526
13.3%
I1154
10.1%
t1154
10.1%
e1154
10.1%
r1154
10.1%
a1154
10.1%
P372
 
3.2%
u372
 
3.2%
b372
 
3.2%
Other values (2)744
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11464
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2308
20.1%
l1526
13.3%
I1154
10.1%
t1154
10.1%
e1154
10.1%
r1154
10.1%
a1154
10.1%
P372
 
3.2%
u372
 
3.2%
b372
 
3.2%
Other values (2)744
 
6.5%

Status
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing62
Missing (%)3.9%
Memory size12.5 KiB
Close Budget
788 
WO
290 
Close WO
262 
On Going
97 
Draft
 
67

Length

Max length12
Median length12
Mean length8.797235023
Min length2

Characters and Unicode

Total characters13363
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOn Going
2nd rowOn Going
3rd rowWO
4th rowWO
5th rowWO
ValueCountFrequency (%)
Close Budget788
49.8%
WO290
 
18.3%
Close WO262
 
16.6%
On Going97
 
6.1%
Draft67
 
4.2%
Canceled15
 
0.9%
(Missing)62
 
3.9%
2021-02-03T13:02:05.761654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-03T13:02:06.014385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
close1050
39.4%
budget788
29.6%
wo552
20.7%
on97
 
3.6%
going97
 
3.6%
draft67
 
2.5%
canceled15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e1868
14.0%
1147
8.6%
o1147
8.6%
C1065
 
8.0%
l1065
 
8.0%
s1050
 
7.9%
g885
 
6.6%
t855
 
6.4%
d803
 
6.0%
B788
 
5.9%
Other values (11)2690
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8998
67.3%
Uppercase Letter3218
 
24.1%
Space Separator1147
 
8.6%

Most frequent character per category

ValueCountFrequency (%)
e1868
20.8%
o1147
12.7%
l1065
11.8%
s1050
11.7%
g885
9.8%
t855
9.5%
d803
8.9%
u788
8.8%
n209
 
2.3%
i97
 
1.1%
Other values (4)231
 
2.6%
ValueCountFrequency (%)
C1065
33.1%
B788
24.5%
O649
20.2%
W552
17.2%
G97
 
3.0%
D67
 
2.1%
ValueCountFrequency (%)
1147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12216
91.4%
Common1147
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
e1868
15.3%
o1147
9.4%
C1065
8.7%
l1065
8.7%
s1050
8.6%
g885
7.2%
t855
7.0%
d803
6.6%
B788
6.5%
u788
6.5%
Other values (10)1902
15.6%
ValueCountFrequency (%)
1147
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13363
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1868
14.0%
1147
8.6%
o1147
8.6%
C1065
 
8.0%
l1065
 
8.0%
s1050
 
7.9%
g885
 
6.6%
t855
 
6.4%
d803
 
6.0%
B788
 
5.9%
Other values (11)2690
20.1%

Tgl Mulai
Categorical

HIGH CARDINALITY

Distinct271
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2020-10-22
 
17
2020-02-24
 
17
2020-04-16
 
16
2020-11-24
 
16
2020-02-03
 
15
Other values (266)
1500 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters15810
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)2.5%

Sample

1st row2020-12-16
2nd row2020-12-16
3rd row2020-12-16
4th row2020-12-16
5th row2020-12-16
ValueCountFrequency (%)
2020-10-2217
 
1.1%
2020-02-2417
 
1.1%
2020-04-1616
 
1.0%
2020-11-2416
 
1.0%
2020-02-0315
 
0.9%
2020-06-0815
 
0.9%
2020-10-1915
 
0.9%
2020-02-1015
 
0.9%
2020-02-0614
 
0.9%
2020-09-0714
 
0.9%
Other values (261)1427
90.3%
2021-02-03T13:02:07.248916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-10-2217
 
1.1%
2020-02-2417
 
1.1%
2020-04-1616
 
1.0%
2020-11-2416
 
1.0%
2020-02-0315
 
0.9%
2020-06-0815
 
0.9%
2020-10-1915
 
0.9%
2020-02-1015
 
0.9%
2020-02-0614
 
0.9%
2020-09-0714
 
0.9%
Other values (261)1427
90.3%

Most occurring characters

ValueCountFrequency (%)
05130
32.4%
24064
25.7%
-3162
20.0%
11429
 
9.0%
6325
 
2.1%
9305
 
1.9%
3303
 
1.9%
7293
 
1.9%
8282
 
1.8%
4259
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12648
80.0%
Dash Punctuation3162
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
05130
40.6%
24064
32.1%
11429
 
11.3%
6325
 
2.6%
9305
 
2.4%
3303
 
2.4%
7293
 
2.3%
8282
 
2.2%
4259
 
2.0%
5258
 
2.0%
ValueCountFrequency (%)
-3162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15810
100.0%

Most frequent character per script

ValueCountFrequency (%)
05130
32.4%
24064
25.7%
-3162
20.0%
11429
 
9.0%
6325
 
2.1%
9305
 
1.9%
3303
 
1.9%
7293
 
1.9%
8282
 
1.8%
4259
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15810
100.0%

Most frequent character per block

ValueCountFrequency (%)
05130
32.4%
24064
25.7%
-3162
20.0%
11429
 
9.0%
6325
 
2.1%
9305
 
1.9%
3303
 
1.9%
7293
 
1.9%
8282
 
1.8%
4259
 
1.6%

Tgl Selesai
Categorical

HIGH CARDINALITY

Distinct310
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
2020-10-23
 
20
2020-09-18
 
18
2020-06-25
 
17
2020-05-15
 
15
2020-10-27
 
15
Other values (305)
1496 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters15810
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)4.5%

Sample

1st row2020-12-17
2nd row2020-12-18
3rd row2021-01-16
4th row2021-03-16
5th row2021-03-03
ValueCountFrequency (%)
2020-10-2320
 
1.3%
2020-09-1818
 
1.1%
2020-06-2517
 
1.1%
2020-05-1515
 
0.9%
2020-10-2715
 
0.9%
2020-06-2615
 
0.9%
2020-02-0715
 
0.9%
2020-06-1914
 
0.9%
2020-03-0614
 
0.9%
2020-09-2414
 
0.9%
Other values (300)1424
90.1%
2021-02-03T13:02:08.007714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-10-2320
 
1.3%
2020-09-1818
 
1.1%
2020-06-2517
 
1.1%
2020-05-1515
 
0.9%
2020-10-2715
 
0.9%
2020-06-2615
 
0.9%
2020-02-0715
 
0.9%
2020-06-1914
 
0.9%
2020-03-0614
 
0.9%
2020-09-2414
 
0.9%
Other values (300)1424
90.1%

Most occurring characters

ValueCountFrequency (%)
05077
32.1%
24045
25.6%
-3162
20.0%
11388
 
8.8%
6363
 
2.3%
3352
 
2.2%
9333
 
2.1%
7301
 
1.9%
8289
 
1.8%
4258
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12648
80.0%
Dash Punctuation3162
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
05077
40.1%
24045
32.0%
11388
 
11.0%
6363
 
2.9%
3352
 
2.8%
9333
 
2.6%
7301
 
2.4%
8289
 
2.3%
4258
 
2.0%
5242
 
1.9%
ValueCountFrequency (%)
-3162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15810
100.0%

Most frequent character per script

ValueCountFrequency (%)
05077
32.1%
24045
25.6%
-3162
20.0%
11388
 
8.8%
6363
 
2.3%
3352
 
2.2%
9333
 
2.1%
7301
 
1.9%
8289
 
1.8%
4258
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII15810
100.0%

Most frequent character per block

ValueCountFrequency (%)
05077
32.1%
24045
25.6%
-3162
20.0%
11388
 
8.8%
6363
 
2.3%
3352
 
2.2%
9333
 
2.1%
7301
 
1.9%
8289
 
1.8%
4258
 
1.6%

JML Peserta
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct167
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.16318786
Minimum0
Maximum1576
Zeros278
Zeros (%)17.6%
Memory size12.5 KiB
2021-02-03T13:02:08.268035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q329
95-th percentile129
Maximum1576
Range1576
Interquartile range (IQR)28

Descriptive statistics

Standard deviation94.04429302
Coefficient of variation (CV)2.923973004
Kurtosis116.8836937
Mean32.16318786
Median Absolute Deviation (MAD)9
Skewness9.414685922
Sum50850
Variance8844.329049
MonotocityNot monotonic
2021-02-03T13:02:08.555949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0278
 
17.6%
1191
 
12.1%
2102
 
6.5%
368
 
4.3%
552
 
3.3%
441
 
2.6%
1538
 
2.4%
2633
 
2.1%
2533
 
2.1%
2033
 
2.1%
Other values (157)712
45.0%
ValueCountFrequency (%)
0278
17.6%
1191
12.1%
2102
 
6.5%
368
 
4.3%
441
 
2.6%
ValueCountFrequency (%)
15761
0.1%
14321
0.1%
10601
0.1%
10501
0.1%
10461
0.1%

JML Confirmed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct107
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.94054396
Minimum0
Maximum1209
Zeros613
Zeros (%)38.8%
Memory size12.5 KiB
2021-02-03T13:02:08.921721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q321
95-th percentile62
Maximum1209
Range1209
Interquartile range (IQR)21

Descriptive statistics

Standard deviation64.47525485
Coefficient of variation (CV)3.404086756
Kurtosis151.1675974
Mean18.94054396
Median Absolute Deviation (MAD)3
Skewness10.99071376
Sum29945
Variance4157.058488
MonotocityNot monotonic
2021-02-03T13:02:09.396029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0613
38.8%
1109
 
6.9%
259
 
3.7%
543
 
2.7%
2033
 
2.1%
332
 
2.0%
430
 
1.9%
2430
 
1.9%
1529
 
1.8%
2225
 
1.6%
Other values (97)578
36.6%
ValueCountFrequency (%)
0613
38.8%
1109
 
6.9%
259
 
3.7%
332
 
2.0%
430
 
1.9%
ValueCountFrequency (%)
12091
0.1%
9831
0.1%
7841
0.1%
7341
0.1%
7261
0.1%

JML Peserta Hadir
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct141
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.47691335
Minimum0
Maximum790
Zeros520
Zeros (%)32.9%
Memory size12.5 KiB
2021-02-03T13:02:09.711967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q325
95-th percentile89
Maximum790
Range790
Interquartile range (IQR)25

Descriptive statistics

Standard deviation61.81022614
Coefficient of variation (CV)2.632808889
Kurtosis79.48242574
Mean23.47691335
Median Absolute Deviation (MAD)5
Skewness7.822408809
Sum37117
Variance3820.504055
MonotocityNot monotonic
2021-02-03T13:02:10.182997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0520
32.9%
1121
 
7.7%
262
 
3.9%
538
 
2.4%
334
 
2.2%
2432
 
2.0%
1531
 
2.0%
2031
 
2.0%
2527
 
1.7%
1027
 
1.7%
Other values (131)658
41.6%
ValueCountFrequency (%)
0520
32.9%
1121
 
7.7%
262
 
3.9%
334
 
2.2%
425
 
1.6%
ValueCountFrequency (%)
7901
0.1%
7781
0.1%
7671
0.1%
7231
0.1%
7081
0.1%

JML UBPP Inst
Real number (ℝ≥0)

ZEROS

Distinct81
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.270714738
Minimum0
Maximum336
Zeros748
Zeros (%)47.3%
Memory size12.5 KiB
2021-02-03T13:02:10.696092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q313
95-th percentile38
Maximum336
Range336
Interquartile range (IQR)13

Descriptive statistics

Standard deviation20.69889825
Coefficient of variation (CV)2.23271871
Kurtosis96.03708196
Mean9.270714738
Median Absolute Deviation (MAD)1
Skewness7.722895144
Sum14657
Variance428.4443887
MonotocityNot monotonic
2021-02-03T13:02:10.981048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0748
47.3%
192
 
5.8%
248
 
3.0%
544
 
2.8%
340
 
2.5%
436
 
2.3%
1035
 
2.2%
929
 
1.8%
1427
 
1.7%
2427
 
1.7%
Other values (71)455
28.8%
ValueCountFrequency (%)
0748
47.3%
192
 
5.8%
248
 
3.0%
340
 
2.5%
436
 
2.3%
ValueCountFrequency (%)
3361
0.1%
3301
0.1%
2531
0.1%
1921
0.1%
1791
0.1%

JML UBPP Delivery
Real number (ℝ≥0)

ZEROS

Distinct104
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.88614801
Minimum0
Maximum343
Zeros645
Zeros (%)40.8%
Memory size12.5 KiB
2021-02-03T13:02:11.306055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q316
95-th percentile47
Maximum343
Range343
Interquartile range (IQR)16

Descriptive statistics

Standard deviation28.01475094
Coefficient of variation (CV)2.174020578
Kurtosis45.42879977
Mean12.88614801
Median Absolute Deviation (MAD)2
Skewness5.735169324
Sum20373
Variance784.82627
MonotocityNot monotonic
2021-02-03T13:02:11.776000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0645
40.8%
1106
 
6.7%
254
 
3.4%
550
 
3.2%
630
 
1.9%
1030
 
1.9%
1129
 
1.8%
928
 
1.8%
327
 
1.7%
1527
 
1.7%
Other values (94)555
35.1%
ValueCountFrequency (%)
0645
40.8%
1106
 
6.7%
254
 
3.4%
327
 
1.7%
425
 
1.6%
ValueCountFrequency (%)
3431
0.1%
3371
0.1%
2701
0.1%
2541
0.1%
2461
0.1%

UBPP Inst
Real number (ℝ≥0)

MISSING
ZEROS

Distinct572
Distinct (%)37.2%
Missing42
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean41.62499675
Minimum0
Maximum100
Zeros761
Zeros (%)48.1%
Memory size12.5 KiB
2021-02-03T13:02:12.201633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median61.67
Q382.915
95-th percentile92.103
Maximum100
Range100
Interquartile range (IQR)82.915

Descriptive statistics

Standard deviation41.65013516
Coefficient of variation (CV)1.000603926
Kurtosis-1.930468971
Mean41.62499675
Median Absolute Deviation (MAD)38.33
Skewness0.03918076167
Sum64060.87
Variance1734.733759
MonotocityNot monotonic
2021-02-03T13:02:12.686598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
48.1%
8016
 
1.0%
9012
 
0.8%
10012
 
0.8%
68.577
 
0.4%
77.147
 
0.4%
91.256
 
0.4%
85.716
 
0.4%
956
 
0.4%
705
 
0.3%
Other values (562)701
44.3%
(Missing)42
 
2.7%
ValueCountFrequency (%)
0761
48.1%
22.861
 
0.1%
301
 
0.1%
351
 
0.1%
50.861
 
0.1%
ValueCountFrequency (%)
10012
0.8%
99.171
 
0.1%
98.332
 
0.1%
97.52
 
0.1%
97.061
 
0.1%

UBPP Akom
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct301
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.29641999
Minimum0
Maximum100
Zeros1030
Zeros (%)65.1%
Memory size12.5 KiB
2021-02-03T13:02:13.138928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371.98
95-th percentile92.5
Maximum100
Range100
Interquartile range (IQR)71.98

Descriptive statistics

Standard deviation38.44866457
Coefficient of variation (CV)1.408560705
Kurtosis-1.256530441
Mean27.29641999
Median Absolute Deviation (MAD)0
Skewness0.77769379
Sum43155.64
Variance1478.299807
MonotocityNot monotonic
2021-02-03T13:02:13.552716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01030
65.1%
10048
 
3.0%
9032
 
2.0%
8023
 
1.5%
7021
 
1.3%
7513
 
0.8%
5013
 
0.8%
8510
 
0.6%
895
 
0.3%
725
 
0.3%
Other values (291)381
 
24.1%
ValueCountFrequency (%)
01030
65.1%
4.173
 
0.2%
7.51
 
0.1%
102
 
0.1%
303
 
0.2%
ValueCountFrequency (%)
10048
3.0%
99.231
 
0.1%
98.671
 
0.1%
98.181
 
0.1%
97.91
 
0.1%

UBPP Sarana
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct374
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.22409235
Minimum0
Maximum100
Zeros646
Zeros (%)40.9%
Memory size12.5 KiB
2021-02-03T13:02:13.948899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median80
Q389.09
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)89.09

Descriptive statistics

Standard deviation43.12527161
Coefficient of variation (CV)0.8418943047
Kurtosis-1.836799094
Mean51.22409235
Median Absolute Deviation (MAD)14.87
Skewness-0.3064904469
Sum80985.29
Variance1859.789051
MonotocityNot monotonic
2021-02-03T13:02:14.295684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0646
40.9%
10084
 
5.3%
9080
 
5.1%
8046
 
2.9%
8535
 
2.2%
7017
 
1.1%
9517
 
1.1%
86.6716
 
1.0%
87.512
 
0.8%
83.3310
 
0.6%
Other values (364)618
39.1%
ValueCountFrequency (%)
0646
40.9%
304
 
0.3%
503
 
0.2%
54.291
 
0.1%
561
 
0.1%
ValueCountFrequency (%)
10084
5.3%
99.331
 
0.1%
99.231
 
0.1%
98.751
 
0.1%
98.181
 
0.1%

UBPP Laborat
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1581
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
01581
100.0%
2021-02-03T13:02:15.053796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-03T13:02:15.272525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
01581
100.0%

Most occurring characters

ValueCountFrequency (%)
01581
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1581
100.0%

Most frequent character per category

ValueCountFrequency (%)
01581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1581
100.0%

Most frequent character per script

ValueCountFrequency (%)
01581
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1581
100.0%

Most frequent character per block

ValueCountFrequency (%)
01581
100.0%

UBPP Penyelenggaraan
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct530
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.38854522
Minimum0
Maximum100
Zeros645
Zeros (%)40.8%
Memory size12.5 KiB
2021-02-03T13:02:15.523761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83.33
Q390
95-th percentile96.67
Maximum100
Range100
Interquartile range (IQR)90

Descriptive statistics

Standard deviation43.81990447
Coefficient of variation (CV)0.836440567
Kurtosis-1.845590007
Mean52.38854522
Median Absolute Deviation (MAD)11.2
Skewness-0.3357931155
Sum82826.29
Variance1920.184028
MonotocityNot monotonic
2021-02-03T13:02:16.013138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0645
40.8%
10056
 
3.5%
9032
 
2.0%
9219
 
1.2%
8817
 
1.1%
9414
 
0.9%
8613
 
0.8%
8211
 
0.7%
9811
 
0.7%
8011
 
0.7%
Other values (520)752
47.6%
ValueCountFrequency (%)
0645
40.8%
321
 
0.1%
341
 
0.1%
401
 
0.1%
521
 
0.1%
ValueCountFrequency (%)
10056
3.5%
99.781
 
0.1%
99.331
 
0.1%
99.231
 
0.1%
991
 
0.1%

UBPP Kafetaria
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct294
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.16835547
Minimum0
Maximum100
Zeros1030
Zeros (%)65.1%
Memory size12.5 KiB
2021-02-03T13:02:16.331815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q370.71
95-th percentile92.5
Maximum100
Range100
Interquartile range (IQR)70.71

Descriptive statistics

Standard deviation38.38178732
Coefficient of variation (CV)1.412738705
Kurtosis-1.233079318
Mean27.16835547
Median Absolute Deviation (MAD)0
Skewness0.7889700381
Sum42953.17
Variance1473.161598
MonotocityNot monotonic
2021-02-03T13:02:16.767734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01030
65.1%
10047
 
3.0%
9035
 
2.2%
8024
 
1.5%
7015
 
0.9%
5012
 
0.8%
8511
 
0.7%
759
 
0.6%
73.337
 
0.4%
956
 
0.4%
Other values (284)385
 
24.4%
ValueCountFrequency (%)
01030
65.1%
4.173
 
0.2%
7.51
 
0.1%
103
 
0.2%
201
 
0.1%
ValueCountFrequency (%)
10047
3.0%
99.231
 
0.1%
99.091
 
0.1%
981
 
0.1%
97.691
 
0.1%

UBPP Materi
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct492
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.5327957
Minimum0
Maximum100
Zeros646
Zeros (%)40.9%
Memory size12.5 KiB
2021-02-03T13:02:17.266107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83.63
Q390.1
95-th percentile96.88
Maximum100
Range100
Interquartile range (IQR)90.1

Descriptive statistics

Standard deviation43.97953336
Coefficient of variation (CV)0.8371824262
Kurtosis-1.848114651
Mean52.5327957
Median Absolute Deviation (MAD)11.14
Skewness-0.3353367585
Sum83054.35
Variance1934.199354
MonotocityNot monotonic
2021-02-03T13:02:17.737621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0646
40.9%
10064
 
4.0%
9034
 
2.2%
9522
 
1.4%
92.517
 
1.1%
87.516
 
1.0%
8514
 
0.9%
8013
 
0.8%
82.511
 
0.7%
91.2510
 
0.6%
Other values (482)734
46.4%
ValueCountFrequency (%)
0646
40.9%
32.51
 
0.1%
351
 
0.1%
401
 
0.1%
47.51
 
0.1%
ValueCountFrequency (%)
10064
4.0%
99.781
 
0.1%
99.231
 
0.1%
98.751
 
0.1%
98.641
 
0.1%

Net Promotor Score
Real number (ℝ)

ZEROS

Distinct248
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.36956357
Minimum-100
Maximum100
Zeros693
Zeros (%)43.8%
Memory size12.5 KiB
2021-02-03T13:02:18.236990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile0
Q10
median42.86
Q375
95-th percentile100
Maximum100
Range200
Interquartile range (IQR)75

Descriptive statistics

Standard deviation40.70938563
Coefficient of variation (CV)1.034031926
Kurtosis-1.039682548
Mean39.36956357
Median Absolute Deviation (MAD)42.86
Skewness0.02473231594
Sum62243.28
Variance1657.254079
MonotocityNot monotonic
2021-02-03T13:02:18.702532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0693
43.8%
100205
 
13.0%
66.6746
 
2.9%
5044
 
2.8%
7524
 
1.5%
8022
 
1.4%
4015
 
0.9%
6014
 
0.9%
83.3311
 
0.7%
9010
 
0.6%
Other values (238)497
31.4%
ValueCountFrequency (%)
-1007
0.4%
-84.481
 
0.1%
-502
 
0.1%
-33.332
 
0.1%
-252
 
0.1%
ValueCountFrequency (%)
100205
13.0%
98.441
 
0.1%
97.181
 
0.1%
95.831
 
0.1%
94.871
 
0.1%

Customer Satisfaction
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct580
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.62841872
Minimum0
Maximum100
Zeros646
Zeros (%)40.9%
Memory size12.5 KiB
2021-02-03T13:02:19.170100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median77.78
Q388.33
95-th percentile96.67
Maximum100
Range100
Interquartile range (IQR)88.33

Descriptive statistics

Standard deviation42.6850379
Coefficient of variation (CV)0.8431043073
Kurtosis-1.833236423
Mean50.62841872
Median Absolute Deviation (MAD)16.03
Skewness-0.2984664048
Sum80043.53
Variance1822.01246
MonotocityNot monotonic
2021-02-03T13:02:19.643650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0646
40.9%
10059
 
3.7%
9025
 
1.6%
8017
 
1.1%
83.3314
 
0.9%
8813
 
0.8%
93.3311
 
0.7%
8610
 
0.6%
86.679
 
0.6%
929
 
0.6%
Other values (570)768
48.6%
ValueCountFrequency (%)
0646
40.9%
26.671
 
0.1%
301
 
0.1%
341
 
0.1%
36.671
 
0.1%
ValueCountFrequency (%)
10059
3.7%
99.631
 
0.1%
99.231
 
0.1%
98.891
 
0.1%
98.361
 
0.1%

First Response & Average Handling
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct351
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.67101202
Minimum0
Maximum100
Zeros646
Zeros (%)40.9%
Memory size12.5 KiB
2021-02-03T13:02:20.138221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83.75
Q390
95-th percentile99.78
Maximum100
Range100
Interquartile range (IQR)90

Descriptive statistics

Standard deviation44.11103567
Coefficient of variation (CV)0.8374822123
Kurtosis-1.846399763
Mean52.67101202
Median Absolute Deviation (MAD)11.81
Skewness-0.3326351765
Sum83272.87
Variance1945.783468
MonotocityNot monotonic
2021-02-03T13:02:20.563008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0646
40.9%
9093
 
5.9%
10079
 
5.0%
8034
 
2.2%
8521
 
1.3%
9520
 
1.3%
93.3314
 
0.9%
86.6713
 
0.8%
9213
 
0.8%
83.3312
 
0.8%
Other values (341)636
40.2%
ValueCountFrequency (%)
0646
40.9%
401
 
0.1%
505
 
0.3%
601
 
0.1%
621
 
0.1%
ValueCountFrequency (%)
10079
5.0%
99.781
 
0.1%
99.231
 
0.1%
99.091
 
0.1%
991
 
0.1%

Customer Effort Score
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct514
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.27979127
Minimum0
Maximum100
Zeros646
Zeros (%)40.9%
Memory size12.5 KiB
2021-02-03T13:02:21.633080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median83.25
Q390
95-th percentile96.25
Maximum100
Range100
Interquartile range (IQR)90

Descriptive statistics

Standard deviation43.76901974
Coefficient of variation (CV)0.8372072397
Kurtosis-1.847532512
Mean52.27979127
Median Absolute Deviation (MAD)10.75
Skewness-0.3349355548
Sum82654.35
Variance1915.727089
MonotocityNot monotonic
2021-02-03T13:02:22.001721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0646
40.9%
10057
 
3.6%
9032
 
2.0%
9225
 
1.6%
8822
 
1.4%
8618
 
1.1%
8015
 
0.9%
9415
 
0.9%
8414
 
0.9%
919
 
0.6%
Other values (504)728
46.0%
ValueCountFrequency (%)
0646
40.9%
321
 
0.1%
381
 
0.1%
401
 
0.1%
481
 
0.1%
ValueCountFrequency (%)
10057
3.6%
99.731
 
0.1%
99.331
 
0.1%
99.231
 
0.1%
991
 
0.1%

Interactions

2021-02-03T13:00:49.546072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:49.744129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:49.936622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.122120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.287678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.474214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.632792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.807252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:50.974861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.143029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.306421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.479918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.653278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.811480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:51.992000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:52.160547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:52.342062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:52.524580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:52.714068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:52.924111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:53.155588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:53.369357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:53.624007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:53.831452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:54.235912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:54.703750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:55.058731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:55.389757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:55.695570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:56.067139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:56.363562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:56.727873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:57.069973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:57.421623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:57.714387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:58.028596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:58.284891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:58.541801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:58.992621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:59.245980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:59.497656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:00:59.760616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:00.244662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:00.576841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:00.915661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:01.280993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:01.616554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:01.954862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:02.260329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:02.565954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:02.883600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:03.177664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:03.498729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:03.929571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:04.231632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:04.511217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:04.740868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:05.013686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:05.237040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:05.457633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:05.683729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:05.917597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:06.153052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:06.375544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:06.612975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:06.835923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:07.036536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:07.249807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:07.457706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:07.727033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:08.015701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:08.248794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:08.518957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:08.737620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:08.973689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:09.184779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:09.407956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:09.643817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:09.870941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:10.130820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:10.384194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:10.635072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:10.870947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:11.100392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:11.337666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:11.559919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:11.776014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:12.035702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:12.264855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:12.496793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:12.729089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:12.957486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:13.180741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:13.398605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:13.910718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:14.137311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:14.354206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:14.553814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:14.742454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:14.940374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:15.136995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:15.335771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:15.552980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:15.744623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:15.949663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:16.144072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:16.345244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:16.550678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:16.785749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:17.032759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:17.243510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:17.553667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:17.749800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:17.946814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:18.136614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:18.382527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:18.708656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:19.102693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:19.402043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:19.735511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:20.012355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:20.259468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:20.535684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:21.829335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:22.071571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:22.555272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:22.793158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:23.078085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:23.325216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:23.557572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:23.792986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:24.051634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:24.335732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:24.591727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:24.864262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:25.118766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:25.364987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:25.613830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:25.851748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:26.092421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:26.333689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:26.571840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:26.826277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:27.079436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:27.355971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:27.856238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:28.114569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:28.403725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:28.686049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:28.909996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:29.109008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:29.296980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:29.485402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:29.681049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:29.876441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:30.063464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:30.277578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:30.471166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:30.670786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:30.870013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:31.331298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:31.534367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:31.725765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:31.934015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:32.126856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:32.327770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:32.524104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:32.710383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:32.899805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:33.079381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:33.282974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:33.512006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:33.699491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:33.996046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:34.233972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:34.584807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:34.928705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:35.173046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:36.759762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:37.071736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:37.426021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:37.761631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:38.099810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:38.464267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:39.629175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:40.871644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:41.161539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:41.401692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:41.671158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:41.911252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:42.970693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:43.316803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:43.746802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:44.193041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-03T13:01:45.465355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:45.916878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:46.319630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:46.701865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:47.133032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:47.544668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:47.884303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:48.281693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:48.658579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:49.086855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:49.488444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:49.943664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:50.331846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:50.732136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:51.166596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:51.594080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:51.961617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:52.437356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:52.881851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:53.281749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:53.575608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:53.887293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:54.305413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-03T13:01:54.611757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-03T13:02:22.438671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-03T13:02:23.444871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-03T13:02:24.385687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-03T13:02:25.395749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-03T13:02:26.301977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-03T13:01:55.343302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-03T13:01:58.042854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-03T13:01:58.751154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-03T13:01:59.305118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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1578event tahun 202090003690NON LATSharing Session / Bedah BukuBandungLearning Area 3 / LO BandungNaNLA 3InternalInternalDraft2020-01-012020-01-06000000.00.00.000.00.00.00.00.00.00.0
1579CX Professional Forrester International90004178NON LATNaNNaNNaNCONSUMERNaNNaNInternalDraft2020-01-012020-01-01100000.00.00.000.00.00.00.00.00.00.0
1580SITAC & Litigasi80113475LATNaNNaNNaNNaNOnline (Lokasi Masing-Masing)NaNNaNNaN2020-01-012020-01-02000000.00.00.000.00.00.00.00.00.00.0